Modeling route choice with advanced traveler information by Fuzzy Logic

Teodorović, Dušan; Vukanović, Smiljan M.; Obradovic, Katarina B.

doi:10.1080/03081069808717617

Cited by 22 publications

(6 citation statements)

References 13 publications

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“…Hawas (2004) cites a number of studies which attempted to address these limitations (e.g. Teodorovic et al 1998, Henn 2000, Kuri and Pursula 2001, but reports that they have met with limited success.…”

Section: Limitations Of Existing Approachesmentioning

confidence: 95%

Evaluation of discrete choice and neural network approaches for modelling driver compliance with traffic information

Dia

Panwai

2009

TTRA

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This article evaluates dynamic driver behaviour models that can be used, in the context of intelligent transport systems (ITS), to predict drivers' compliance with traffic information. The inputs to this type of models comprise drivers' individual socio-economic characteristics and other variables that may influence their compliance behaviour. The output is a binary integer representing whether drivers comply with travel advice or not. Two approaches are available for formulating this category of classification problems: discrete choice models and artificial neural networks (ANNs). The literature on this topic clearly points to the limitations of the discrete choice approach which suffers from assumptions of perfect information about travel conditions, infinite information processing capabilities of drivers and inability to model the uncertainty in driver decision making or the vagueness in information received from ITS devices. ANNs, on the other hand, are able to deal with complex non-linear relationships, are fault tolerant in producing acceptable results under imperfect inputs and are suitable for modelling reactive behaviour which is often described using rules, linking a perceived situation with appropriate action. This study aims to evaluate the performance of these two categories of models based on a common data set of driver behaviour, collected from a field behavioural survey on a congested commuting corridor in Brisbane, Australia. This article proposes the combination of fuzzy logic and neural networks as a viable approach for overcoming the limitations of existing algorithms by modelling drivers as heterogeneous individuals. The results showed superior performance for a neuro-fuzzy model over binary choice models in terms of classifying or predicting the categories of drivers most likely to comply (or not comply) with traffic advice. The accuracy of the proposed model, in terms of classification rate, ranged between 95 and 97% compared to 50-73% for the discrete choice models.

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Section: Limitations Of Existing Approachesmentioning

confidence: 95%

Evaluation of discrete choice and neural network approaches for modelling driver compliance with traffic information

Dia

Panwai

2009

TTRA

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“…In particular, the influence of information on drivers route choice was early presented in (Teodorović et al, 1998) where the effects due to the presence of ATIS was also simulated.…”

Section: Route Choice Modelsmentioning

confidence: 99%

Handling uncertainty in route choice models: From probabilistic to possibilistic approaches

Henn

Ottomanelli

2006

European Journal of Operational Research

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“…како би били боље ин-формисани када доносе одлуке о путовању [4,5,6]. Стога се користи које возачи остварују од ATIS-а огледају у: повећању или кориговању зна-ња, смањењу неизвесности о очекиваним усло-вима саобраћаја током путовања, и тиме повећању ефикасности одлучивања.…”

Section: напредни системи за информисање возачаunclassified